A decision support system for evaluation of wildlife habitat

A decision support system for evaluation of wildlife habitat

Ecological Modelling 102 (1997) 287 – 300 A decision support system for evaluation of wildlife habitat Luis A. Garcia a,*, Michael Armbruster b a De...

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Ecological Modelling 102 (1997) 287 – 300

A decision support system for evaluation of wildlife habitat Luis A. Garcia a,*, Michael Armbruster b a

Department of Chemical and Bioresource Engineering, Colorado State Uni6ersity, Fort Collins, CO 80523, USA United States Bureau of Reclamation, Den6er Federal Center, Building 67, P.O. Box 25007 (D-8210), Den6er, CO 80225 -0007, USA

b

Accepted 14 March 1997

Abstract A decision support system (DSS) for the evaluation of wildlife habitat suitability based on the spatial characteristics of habitat has been developed and implemented on a UNIX workstation. The system uses a geographic information system for the manipulation of spatial information. At the present time, the system is using several habitat models to quantify the quality of wildlife habitat. This paper describes the theoretical background of the system and provides a case study for the use of the system. © 1997 Elsevier Science B.V. Keywords: Wildlife habitat; Habitat; Computer modeling; Decision support systems (DSS); Geographic information systems (GIS); UNIX

1. Introduction Wildlife managers often deal with the fundamental issue of quality and quantity of habitat available to animal populations associated with specific land tracts (Giles, 1978). Numerous tools and techniques are available for acquiring habitat and population data (Cooperrider et al., 1986). These tools work well for monitoring studies or the tracking of single species. However, as management activities and goals become more com* Corresponding author. Tel.: + 1 970 4915144; fax: +1 970 4912293.

plex, the interaction of multiple species and their sometimes differing habitat requirements become important considerations. Structured approaches that facilitate data interpretation and decision making for complex studies are limited. Habitat models are tools that can be used to address quality and quantity of habitat. Models can provide both the context for comparison of landscape features and a value system necessary for effective evaluation (Farmer et al., 1982). However, a common weakness of current approaches to modeling wildlife habitat is the inability to effectively address the spatial relationships between the value of landscape features and their

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location in relation to other features such as the distances between potential gadwall (Anas strepera) nesting cover and wetlands (Hienen and Cross, 1983; Cooperrider et al., 1986). The introduction of computerized methods of analyzing spatial information has made it possible to evaluate map data while accounting for the spatial distribution of components. Recent studies (Lyon et al., 1987; Hodgson et al., 1988; McNay and Page, 1990) have demonstrated the utility and cost-effectiveness of evaluating habitat with a geographic information system (GIS). We were interested in combining the spatial analysis capabilities of GIS and the analytical properties of habitat suitability models into an efficient decision tool. This paper describes a means of analyzing and interpreting land cover maps to produce spatially defined data that can be a valuable information source for managing wildlife habitat. The emphasis of this paper is on the technique and not on the often controversial issue of defining habitat quality. It must be understood that the landscape characteristics, which are important for habitat evaluation, vary according to region and specific wildlife species. The various models and variables and their use as discussed in this paper should not necessarily be used in other areas, but models and variables should be determined by wildlife managers familiar with local conditions or from agency handbooks that discuss habitat requirements and preferences of wildlife species (US Fish and Wildlife Service, 1981).

2. Decision support system (DSS) framework A DSS assists the decision maker in recognizing his/her needs and in identifying objectives. The decision maker can then formulate and evaluate different courses of action (scenarios). This enhances the abilities of decision makers to evaluate possible impacts of alternative solutions on a project. This analysis must be coupled with the ability to interpret these projected impacts in terms of realistic objectives. A DSS was developed to link a GIS with a set of habitat models in a framework that allows the user to: (1) evaluate the habitat suitability of

different land areas for different species of wildlife; (2) simulate management activities that modify landscape features; and (3) obtain results from simulated activities in terms of changes in habitat suitability and the associated management costs. This interactive tool affords resource managers the ability to develop ‘what if’ scenarios, and to graphically display changes in habitat suitability resulting from simulated landscape alterations. The DSS employs a graphical user interface (GUI) as the graphics link between the user and the systems various models and data. The mousedriven GUI was developed using the ‘C’ programming language combined with ‘OSF/Motif’ (OSF, 1991) and ‘Xt Intrinsic Libraries’ (Quercia and O’Reilly, 1990) for graphics. With the GUI, resource managers can define information required by various models and analyze data through the use of pull-down menus, icons, and interactive windows (Fig. 1). The top menu bar in Fig. 1 contains several pull-down menus that allow the user to request additional information (Help), change raster size (Controls), select and display maps (Maps), select habitat models (Species) and study site (Locate area). Interactive windows are located along the right side of the display and are activated by ‘action buttons’. These action options allow the user to view model data (Model Data), habitat resources (Survey Map), evaluate existing habitat conditions (Calculate Habitat Suitability Index (HSI)), simulate management actions that change landscape features (What If?), change management cost data (Edit Cost Table), and change sites (Reset Study Area).

3. Habitat suitability models Habitat suitability is often determined by an evaluation of life requirements supplied by landscape features; water, food, and cover (thermal, security, reproduction, etc.). Such evaluations can be efficiently conducted manually for small land units with species of limited mobility. However, for large areas, and mobile species that exploit resources from a variety of landscape features in

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289

Fig. 1. Components of the GUI.

different and distant locations, evaluations become more complex. Models can be used to describe the habitat resources required by a particular species of wildlife. To be effective, models must (1) define the resources to be supplied by habitat, (2) identify the landscape features that supply these resources, and (3) document the response of the animal to the supplied resources. While several modeling approaches exist and this DSS could be adapted to be used in this DSS (such as rules or multivariate analysis), we are most familiar with and decided

to use habitat suitability index models (Schamberger et al., 1982). Habitat suitability index (HSI) is a unitless value between 0 and 1 that estimates habitat conditions based on characteristics of land cover types, with a value of 1 representing optimum conditions. The relationships between HSI and cover types can be described mathematically, thus providing a means of quantifying the effect of habitat management. To be effective, models must (1) define the resources to be supplied by habitat, (2) identify

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the landscape features that supply these resources, and (3) document the response of the animal to the supplied resources. Such models usually employ simple production curves to define the relationships between landscape features and performance measurements for the species of interest. For example, the sharp-tailed grouse (Tympanuchus phasianellus) model models habitat requirements for nesting and brood rearing. The sharp-tailed broods have a certain amount of shade requirement. It has been determined that the optimal amount of shade is provided when 1 – 10% of the area is covered with woody vegetation. When woody vegetation equals or exceeds 75% of the area it is assumed that the areas will be unsuitable for sharp-tailed grouse nesting and brood rearing activities (Schamberger et al., 1982). This relationship between the percent of woody vegetation per section and suitability index for sharp-tailed grouse brood rearing activities is shown in Fig. 2. The brood suitability index is combined with the nesting suitability index to calculate the HSI for sharp-tailed grouse using the following formula: HSI =(V1)2 ×V2)1/3

(1)

Fig. 2. Sharp-tailed grouse brood suitability index value graph.

where V1 is the sharp-tailed grouse nesting suitability index, and V2 is the sharp-tailed grouse brood suitability index. This approach alone works well for simple relationships with landscape features that are uniformly distributed. But, resources are often concentrated in particular areas or clumped, and because of variation in the physical characteristics of landscape features, different resource clumps have different values to the species of interest. Clump value may also depend on location. Location is usually evaluated in terms of the distance an animal would have to travel to obtain resources. In other words, the distance between resource clumps becomes important in determining habitat suitability. Optimum habitat consists of the juxtaposition of landscape features containing high value resources which supply all habitat requirements for the species of interest. When the location of landscape features in relation to each other becomes important, developing an effective habitat model, and applying it to the situation, becomes more challenging. Historically, model data were obtained manually from site surveys and map computations. Landscape features identified in the model were located, surveyed, and assigned a value defined by the relationships between their physical characteristics and the habitat resource(s) supplied. This modified resource value was then applied to the site under consideration and the new value becomes the quantified habitat value of the site for the species of interest. Manual application of the above approach is often laborious and prone to errors that make replication of results difficult. The technology of remote sensing has provided the means for mapping land cover/vegetation over very large areas. However, the maps themselves supply only part of the inventory data needed by biologists who manage wildlife habitat. The vegetation maps must be analyzed and interpreted to enhance the various characteristics of the landscape which have a bearing on management decisions. In short, the standard land cover maps are a source of information that may be helpful in making management decisions, but they must be interpreted. Habitat models coupled with a GIS can provide a mechanism for this interpretation.

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291

Table 1 Data used in the HSI model Abbreviation

Complete name

Wetland =1 otherwise 0

Visual obstruction readings (VOR)

Nesting quality index (NQI)

Categories in GIS map

W TG NG HW-1 HW-2 HW-3 HW-4 HW-5 HW-9 OW

Woodlands Tame grasslands Native grasslands Type 1 wetlands Type 2 wetlands Type 3 wetlands Type 4 wetlands Type 5 wetlands Type 9 wetlands Open water

0 0 0 1 1 1 1 1 1 1

0.0 2.0 1.8 0.5 0.5 0.0 0.0 0.0 0.0 0.0

0.0 0.9 0.8 0.3 0.0 0.0 0.0 0.0 0.0 0.0

C

Cropland

0

0.15

0.13

F PH A

Forbland Pasture/haylands Alfalfa

0 0 0

0.15 0.4 0.2

0.2 0.2 0.1

24 30 29, 1, 2 3, 4, 5 9 6, 19, 28, 40, 36 37, 27

4. GIS implementation of habitat suitability models In order to implement a computer model based on habitat resources, a map of vegetation coverage must be developed. To generalize the application of the computer model to existing maps, a filter was created to combine types of ground cover found in existing maps to form categories that are requested by the models. For instance, cropland is a type of cover required for model input and, since the type of cropland is not important to the model, all cropland without considering different types of crops is given the designation cropland. This is done by generating a conversion table that relates the existing categories in a map to the required categories for each of the models. The last column of Table 1 shows all the categories that are combined to form the category shown in column 1 for the map used in the case study that will be explained later in the paper. This filter also provides a simple mechanism for specifying other parameters relating to each cover type such as visual obstruction readings (VOR), or nesting quality index (NQI) which are parameters used in the habitat models and will be described later in the text.

49, 23 18 17, 22, 53, 54 15, 20, 55

8, 10, 11, 21, 50, 51, 52 31, 32, 33, 34, 41, 42, 12 47, 13, 14, 16

In addition to the vegetation map, each habitat model must be studied to develop an algorithm for the computation of habitat suitability using GIS. The models used in this system were very different and to facilitate the development of all the models a simple framework was developed to allow for the addition of new models. The procedure that was developed for the conversion of the different habitat models to a GIS-based spatial context required the linkage of each of the models with specific map characteristics. First, a description file was created that contains the full name of the species, the cover types it depends on, and the minimum habitat area (the minimum amount of

Table 2 Habitat model definition information Species name Cover types Minimum size Species name Cover types Minimum size Species name Cover types Minimum size

Gray partridge TG, NG, PH, C, F 4 ha Gadwalls TG, PH, C, F, HW-1, HW-2, HW-3, HW-4, HW-5, HW-9, OW 64.8 ha Sharp-tailed grouse A, W, TG, NG, HW-1, HW-2 5.3 km2

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contiguous, suitable habitat that can support this species) as shown in Table 2. The habitat model for gadwalls, a type of waterfowl, defines three habitat requirements: those of pairs, nesting, and broods (Sousa, 1985, 1987), and is a good example of the importance of spatial analysis. The model requires that the minimum potential habitat that can support gadwalls (Anas strepera) be greater than 64 ha. This is implemented in the GIS by denying the user the ability to select a study area smaller than 64 ha when evaluating habitat for gadwalls. After the study area has been selected, the cover types that are present and contribute to the habitat are identified based on the list of contributing cover types shown in Table 2. In order to convey this information to the user, a GIS function called a ‘mask’ is used. A ‘mask’ is simply a binary raster map that contains a ‘1’ in areas where a valid cover type exists and a ‘0’ elsewhere. The ‘mask’ is overlaid on the cover type map, the types that contribute to the habitat are displayed, and the remaining cover types become a background color. This feature rapidly displays the spatial distribution of the contributing habitat. The second step in calculating the habitat suitability for gadwalls is to compute the number and the combined area of different types of wetlands. These values are computed in a three-step process. First, a ‘mask’ map for each wetland category is created and overlaid on the cover type map. Next, each of these maps is ‘clumped’, a process that aggregates a contiguous zone around an area of cover type and assigns a unique number to all the raster cells in the zone. Next, the zones are counted and the area of each zone is calculated. The gadwall model requires that the interspersion of nesting and wetland habitat be calculated. A GIS is particularly suited for this task. This requirement was addressed by utilizing the ‘clumped’ maps generated in the previous phase. Each ‘clumped’ map is converted into a binary map that contains a ‘1’ where cells are within clumps and a ‘0’ elsewhere. Proximity (or buffer zone) maps are then generated that first contain a zone from 0 to 1600 m, and then zones in 200 m increments up to a distance of 3200 m. The gadwall model contains interspersion indices based

on the distance between wetlands greater than 1.2 ha and potential nesting cover types. In order to determine the distance between the nesting cover types and the wetlands, binary nesting cover maps are generated (maps containing ‘1’ where a nesting cover exist and ‘0’ elsewhere) and overlaid on the proximity maps (buffer zone) to generate the interspersion indices (using a GIS function) for each nesting cover type. After obtaining all the spatial information, the system computes the suitability index for gadwall pairs, nesting, and broods. All the spatial information required is obtained using the GIS component of the system. The following are the equations developed for the implementation of the gadwall model as part of the DSS and are based on habitat models described by Sousa (1985). These models evaluate suitability by assessing the gadwall requirements for pairs, nesting, and broods. Habitat suitability for pairs is a function of the availability and distribution of wetland types. An index of preference for wetland classes was developed based on wetland use compared to wetland availability. This index, denoted by pk (k= 1, … ,n), ranges from a value of one, for the highest quality wetland, to a value of zero for cover types that are least suitable for gadwall pairs. The number of wetlands in a study area can be converted to the number of optimum wetlands by using the preference indices for pairs. The number of equivalent optimum wetlands for gadwall pairs, WNg,pr, is given by Eq. (2). WNg,pr =

2.59 n % p ·n size k = 1 k k

(2)

where ‘size’ is the total size of the study area in km2 units, nk is a decision variable representing the number of wetlands of class k, n is the number of wetland types available, and pk is the preference index for gadwall pairs for wetland class k. The suitability index related to the number of wetlands, Sg,pr,wn, has been determined to reach a maximum value of one when the equivalent optimal number of wetlands per 2.59 km2 (1 mi2) exceeds 150. The equations used in the model to compute the suitability index related to the number of wetlands, Sg,pr,wn, are given by Eq. (3).

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WNg,pr 150

Sg,pr,wn =

Sg,pr,wn =1

if (WNg,pr 5150)

if (WNg,pr ]150)

Ng = (3)

The function for the area of wetlands was derived from the equation for wetland numbers by factoring in values for the size of each wetland type. The normalized equivalent optimum area of wetland, WAg,pr, is given by Eq. (4). WAg,pr =

2.59 n % pk · ak size k = 1

(4)

where ‘size’ is the total size of the study area in km2 units, n is the number of wetland types available, ak is the total area of each cover type, and pk is the preference index for gadwall pairs for wetland class k. The suitability index related to the area of wetlands, Sg,pr,wa, has been determined to reach a maximum value of one when the equivalent optimal area of wetlands per 2.59 km2 (1 mi2) exceeds 64. The equations used in the model to compute the suitability index related to the number of wetlands, Sg,pr,wa, are given by Eq. (5). Sg,pr,wa =

WAg,pr 64

Sg,pr,wa = 1

if (WAg,pr 564)

if (WAg,pr ]64)

293

n

(5)

The overall suitability of gadwall pairs (SIg,pr) is the arithmetic average of the suitability indices for the number of wetlands (Sg,pr,wn) and the area of wetlands (Sg,pr,wa). Gadwalls typically select the tallest, most dense, herbaceous vegetation available in which to establish nests. An index of nesting quality, nk (0 5 nk 5 1), was determined for each height-density category based on observed nesting density. The distance from suitable nesting cover to suitable wetland habitat may have an influence on the value of potential nesting cover. Distance measurements can be converted to a mean interspersion index, lk (05lk 51), for each cover type providing potential nesting covers. An estimate of nesting quality is based on the equivalent optimum area of nesting habitat per 2.59 km2 (1 mi2). An estimate of equivalent optimum nesting habitat per 2.59 km2 (one section), Ng, can be determined through Eq. (6).

259.2 % lk · uk · ak size k = 1

(6)

where ‘size’ is the total size of the study area in hectares, ak is the area of cover type k, n is the number of cover types potentially providing gadwall nesting cover, lk is the mean interspersion index for cover type k, and uk the mean nesting quality index for cover type k. The estimate, Ng, must be compared to an optimum condition in order to obtain an index of nesting habitat quality, SIg,ns, for the area being evaluated. Optimum conditions will exist on a hypothetical section if there are 194 ha of optimum nesting habitat. The equations used in the model to compute the normalized equivalent optimum number of wetlands, WNg,pr, are given by Eq. (7). SIg,ns =

Ng 194

if (Ng 5 194) if (Ng 5 194)

SIg,ns = 1

(7)

Habitat suitability for gadwall broods is a function of wetland availability and distribution. Index values indicating the relative quality of each wetland class to gadwall broods, bk (05bk 5 1), were assigned to the wetland classes. Using these preference indices and the area of each wetland class, Eq. (8) determines the equivalent optimum area of wetland per section for broods, denoted by WAg,br. WAg,br =

2.59 n % b ·a size k = 1 k k

(8)

where ‘size’ is the total size of the study area in km2 units, n is the number of wetland types available, ak is the total area of wetland type k, and bk is the preference index for gadwall broods for wetland class k. The suitability index that corresponds to this component, Sg,br,wa, is determined by Eq. (9). SIg,br,wa =

WAg,br 20

SIg,br,wa = 1

if (WAg,br 5 20)

if (WAg,br ] 20)

(9)

A value for the equivalent optimum number of wetlands greater than 0.4 ha per section for broods, WNg,br, can be determined by Eq. (10).

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294 n

WNg,br =

2.59 % tk · wk · bk size k = 1

(10)

where ‘size’ is the total size of the study area in km2 units, n is the number of wetland types available, tk is an integer representing whether wetland k is greater than 0.4 ha or not (0 or 1), wk is the number of equivalent optimal wetlands area of wetland type k, and bk is the preference index for gadwall broods for wetland class k. ti =

!

1 0

if a i ] 1 otherwise

Suitability of brood-rearing habitat (SIg,br,wn) with respect to the number of wetlands is given by Eq. (11). SIg,br,wn =

WNg,br 6

SIg,br,wn =1

if (WNg,br 56)

if (WNg,br ]6)

(11)

The overall suitability of gadwall broods (SIg,br) is the arithmetic average of the suitability indices for the number of wetlands (Sg,br,wn) and the area of wetlands (Sg,br,wa). The production of gadwalls in a particular area is determined by the component with the lowest potential to support the gadwall’s needs. Therefore, the gadwall habitat suitability index, HSIg, is based on the limiting factor and equals the lowest of the values determined for pair, nesting, or brood habitat (Eq. (12)). HSIg =min(SIg,pr, SIg,ns, SIg,br)

(12)

4.1. Habitat management models While the existing habitat suitability of a study area is important information, managers are often interested in estimates of future suitability given natural or man-induced changes in various landscape features. These ‘what if’ scenarios permit the evaluation of potential benefits from activities before commitment of limited management funds. Habitat management models focus on how alterations of landscape features would affect habitat suitability, and how much such actions would cost (Schamberger et al., 1982; Sousa, 1987, 1989). Effective management approaches link habitat suitability and habitat management mod-

els to estimate the costs of changes in suitability. Linkages occur at the habitat variable level. Thus, in the above gadwall example, habitat suitability for nesting could be enhanced by improving vegetation cover or constructing wetlands. Costs of various habitat management actions are discussed in Farmer et al. (1982), and can be developed by local managers. Wildlife management is seldom limited to a single species or single management action. Instead, several species and multiple actions are usually featured in most management plans. In addition, a single landscape feature may be altered by different management actions. For example, vegetation used for nesting cover (a habitat model variable) may be managed by burning, grazing, or periodic replanting. Each action results in a different plant response and carries different costs. Another important consideration in habitat management for multiple species is the effect of management actions on non-target species. For example, the conversion of cropland to native grassland may be viewed as a cost-effective management action for nesting gadwalls. However, species such as the gray partridge (Perdix perdix) and ring-necked pheasant (Phasianus colchicus) that require waste grain as winter food, may be adversely affected by such an action. The spatial analysis capabilities of geographic information systems, habitat suitability models that characterize important landscape features, and habitat management models that identify costs of altering landscape features provide the tools necessary for effective comparisons among alternative habitat management plans.

5. GIS analysis and manipulation of habitat variables The analysis of wildlife habitat is inherently spatial in nature. In general, resources are not randomly distributed, but are clumped. Clumps have different values depending on what is supplied, how much of a resource is available, and how it can be used by different species. Most animals must move among several resource

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clumps to obtain their habitat requirements. Distances between clumps can further modify their value. Ideal conditions exist when resources are juxtaposed, and minimal travel between clumps is required. As the distance between resource clumps increases, the value of an area as habitat decreases. At some distance, clumps are no longer considered habitat because the energy required to obtain resources, and the risks from predation, are too high. A GIS can evaluate these ‘interspersion’ relationships with concentric bands of decreasing value around the landscape feature of interest. The DSS incorporates some of the GIS capabilities of the Geographic Resources Analysis Support System (GRASS) (US Army Corps of Engineers, 1991). This GIS is a raster (cell) system and provides the user with powerful spatial analysis capabilities. Because analysis is directed through the GUI, the user requires no programming skills or detailed knowledge of GIS to effectively use the system. Not only can the DSS determine the habitat suitability from existing conditions, but also provides the option of changing landscape features, and thus changing habitat suitability, to simulate future management actions. For example, if an analysis indicated a particular land tract supported low suitability for nesting gadwalls and their broods, then constructing wetlands near a suitable nesting habitat should improve conditions. This scenario could be simulated with the DSS. The DSS would provide both estimates of changes in suitability for each of the three components of the gadwall model, and estimates of the dollar costs to create those changes (i.e. create wetlands).

6. Application The DSS was developed in a generic fashion to allow its application to different sites. The initial implementation was done around existing data and management goals for the Lonetree Wildlife Management Area (Lonetree). Lonetree is a 13 156-ha (32 890-acre) tract of land located in

295

Fig. 3. Location of the Lonetree wildlife management area.

central North Dakota, USA. The Lonetree site is located between Bismarck and Devils Lake near Harvey, and is situated at the headwaters of the Sheyenne River (Fig. 3). The Lonetree tract was originally purchased in fee title by the United States Government with the intended purpose of a regulatory reservoir as part of the Garrison Diversion Unit Project. Congress has since authorized a much smaller irrigation project, and environmental concerns have altered original plans that included the Lonetree Reservoir. Lonetree Reservoir is still authorized, however, and it or a modified version will be built if an environmentally safe plan can be developed. The database for Lonetree consists of 11 990 individual landscape features in vector format (ARC/INFO). These landscape features were exported from ARC/INFO and converted into a raster format using the GIS, Geographic Resources Analysis support System (GRASS) (US Army Corps of Engineers, 1991; Shapiro et al., 1992). Five habitat suitability index (HSI) models (Schamberger et al., 1982), were selected for this system. The models identify important cover types (vegetation features) used by wildlife, and this information was used to combine features for more efficient system operation (Table 2). Lonetree is a large and diverse area, which includes cropland that is being converted to wildlife habitat, tree and shrub areas, and some tracts of native prairie that have never been ploughed. While all species are important and considered in management, some species can be

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Fig. 4. The DSS with the vegetation map displayed.

used as indicators and the area wildlife manager selected nesting waterfowl (the gadwall), sharptailed grouse, gray partridge, ring-necked pheasant and the white-tailed deer as indicator species for management of Lonetree. The DSS was developed to integrate the capabilities of a GIS and five habitat models. Currently, three of the five habitat models are incorporated into the system (gadwall, sharptailed grouse and gray partridge). The estimated costs of various habitat management activities commonly used to improve habitat quality were also incorporated into the system (Farmer et al., 1988). The following questions address a typical management exercise for a selected site on Lonetree or almost any other wildlife management area. (1)

What is the value of existing habitat for the species X? (2) Why is this value limited? (3) What improvements can be made? (4) What are the costs of the improvements? This application will demonstrate how each of the questions presented above can be approached using the tools available in the DSS. This application will cover a portion of the Lonetree management area. The system allows the user to display a vegetation map for spatial calculations of the location and extent of each cover type (Fig. 4). The DSS allows the user to select any portion of the map for study. The user can then select the species for which the habitat evaluation will be conducted. The DSS provides the user with a menu containing the supported species. Currently, three species

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297

suitability of the area is to determine the area of the contributing cover types. This is done by incorporating GRASS software into the system which ‘masks’ or determines the combined area of the cover types that contribute to the habitat. After this is done, the user can compute the habitat suitability index (HSI) and view the results as shown in Fig. 6. The user can also modify the existing conditions and evaluate the consequences of the modifications.

6.1. Habitat management acti6ities and costs Fig. 5. Choosing a species.

are supported by the system (gadwall, gray partridge, and sharp-tailed grouse), as shown in Fig. 5. The area selected for this case study is 1054 ha (2604 acres) with the Universal Transverse Mercator (UTM) coordinates of North 5 286 991, South 5 283 745, East 408 453, West 405 296 in UTM zone 15. The first step in computing the habitat

Four different scenarios will be evaluated in an attempt to improve habitat conditions for gadwalls without affecting sharp-tailed grouse habitat. Each scenario will be developed under the same constraints; to improve habitat for gadwalls without decreasing the suitability index for sharptailed grouse and limits total cost to under $50 000. A summary of the results is shown in Table 3.

Fig. 6. Displaying nesting data.

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298 Table 3 Scenario results for gadwalls

Type of cover added Type of cover replaced Land change (ha) Total land change (ha) Cost ($) SI before

HSI before SI after

HSI after scenario

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Tame grassland Cropland

Tame and native (1) Tame and native grassland; grassland (2) type-1 wetlands Cropland Cropland

(1) Tame and native grassland; (2) type-1 and type-4 wetlands Cropland

75.6

38 each

(1) 38 each; (2) 12

75.6

76

88

(1) 38 each; (2) 24 type-1 and 22.5 type-4 122.5

26 248

26 260

30 520

42 543 Pair, 0.31 Nesting, 0.26 Brood, 0.64 0.26

Pair, 0.31 Nesting, 0.39 Brood, 0.64 0.31

Pair, 0.31 Nesting, 0.39 Brood, 0.64 0.31

Pair, 0.33 Nesting, 0.38 Brood, 0.62 0.33

In our first scenario, we focused on a simple strategy; adding tame grassland to an area now supporting cropland. Since the model uses the lowest SI of the three life stages (pair, nesting, and brood) as the overall value for the gadwall HSI, we chose this approach to maximize the nesting habitat, the lowest of the three components, since tame grassland has a high value for the NQI and cropland has a low value of NQI as can be seen on Table 1. Using this approach, 74.5 ha (187 acres) of tame grassland were added adjacent to existing grasslands at an estimated total cost of $26 248. These changes resulted in an increase in the suitability index for gadwall nesting from 0.26 to 0.39 with no change to the pair or brood index. Habitat suitability for the sharp-tailed grouse increased from 0.14 to 0.17. For the second scenario, both tame and native grasslands were added in equal proportions at various locations within the cropland cover types (38 ha each) at a cost of $26 260. Using this approach, the nesting index was increased by the same amount as the first scenario (from 0.26 to 0.39), which reflects the similar value of the two preference indexes (0.8 and 0.9). The HSI for the sharp-tailed grouse increased more for this sce-

Pair, 0.41 Nesting, 0.36 Brood, 0.83 0.36

nario than the first one (0.14–0.18), reflecting the higher sharp-tailed grouse preference index for native grasslands. The third scenario added approximately 12 ha of type-1 wetlands in 2-ha plots at a total cost of $4260. The nesting index increased from 0.26 to 0.38 and the pair index increased from 0.31 to 0.33. The HSI is therefore 0.33, the pair index and the lowest value of the three models. This is and improvement to the original HSI (0.33 instead of 0.26) at a cost of $30 520 ($26 260 for the second scenario and $4260 for the additional type-1 wetlands). The fourth scenario added both type-1 wetlands (24 ha) and type-4 wetlands (22.5 ha) at a total cost of $16 283. The nesting index increased from the original but it was less than the results of the third scenario (0.36 instead of 0.38). However, since the pair index increased from 0.31 to 0.41, the lowest value for the models is the nesting index. The overall value for the HSI is now 0.38 and the nesting index value increased from an original value of 0.26 for the HSI. The cost of converting from the original to this scenario is the combined cost of the second and fourth scenarios or $42 543.

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7. Summary and conclusions Because of the spatial aspects of habitat analysis, and the complexity that can be involved in the description of resources, manual application of models is laborious and prone to errors. The use of geographic information systems provides a powerful tool for determining the size and location of resource clumps and the distances between different clumps. This information, the analyses that accompany it, and the software that support it can be efficiently packaged in the form of a decision support system (DSS). A DSS can be an effective mechanism to support technological and managerial decision making. Many of the problems faced by decision makers are becoming more complex and, in many cases, problems are often poorly defined. A DSS can combine multiple sources of information (models and data) into a single system and provide the user with the tools to manipulate the information. With these capabilities, a DSS supports decision makers in the performance of primarily cognitive tasks that involve choices, judgements, and decisions. The DSS discussed here was developed to allow the user access to all relevant data in a convenient and cohesive format, as well as providing support in the form of rules for feasible actions. The system provides the user with a fast and convenient interface for testing ‘what if’ scenarios, thus saving valuable time. The system can provide important input into setting overall goals and objectives for the formulation of specific land management plans. The DSS constitutes a mechanism for integrating environmental resources with other land uses, and assessing trade-offs for wildlife habitat. The DSS also provides guidance with respect to the types and quality of data required for effective management.

8. A look to the future The capabilities of the system are being expanded to include habitat models for at least three more species. An optimization component is being

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developed to determine the optimal use of land for wildlife habitat given specific resource constraints. Acknowledgements This project was funded by the United States Bureau of Reclamation-Water Resources Program. L. Lysne and D. McCabe, Upper Souris Project Office (USBR), and S. Peterson, North Dakota Department of Game and Fish, provided valuable assistance in the conceptual development of the DSS. References Cooperrider, A.Y., Boyd, R.J., Stuard, H.R. (Eds.), 1986. Inventory and Monitoring of Wildlife Habitat. US Department of Interior, Bureau of Land Management Service Center, Denver, CO, p. 858. Farmer, A.H., Armbruster, M.J., Terrell, J.W., Schroeder, R.L., 1982. Habitat models for land-use planning: assumptions and strategies for development. Trans. N. Am. Wildlife Natural Resources Conf. 47, 47 – 56. Hienen, J., Cross, G.H., 1983. An approach to measuring interspersion, juxtaposition and spatial diversity from cover type maps. Wildlife Soc. Bull. 11, 232 – 237. Hodgson, M.E., Jensen, J.R., Mackey, H.E., Coulter, M.C., 1988. Monitoring wood stock foraging habitat using remote sensing and geographic information systems. Photogrammetric Eng. Remote Sensing 54, 1601 – 1607. Farmer, A.H., Matulich, S.C., Hanson, J.E., 1988. Designing cost-effective habitat management plans using optimization methods. Report No. REC-ERC-88-5. US Department of Interior, Bureau of Reclamation, Denver, CO, p. 76. Giles, R.H. Jr., 1978. Wildlife Management. W.H. Freeman and Company, San Francisco, CA, p. 416. Lyon, J.G., Heinen, J.T., Mead, R.A., Roller, N.E., 1987. Spatial data for modeling wildlife habitat. J. Surveying Eng. 113, 88 – 100. McNay, R.S., Page, R.E., 1990. Integrated management of forestry and wildlife habitat with the aid of a GIS-based habitat assessment and planning tool. In: Proceedings of GIS’90 Symposium, Vancouver, British Columbia, pp. 185-190. OSF (Open Software Foundation), 1991. OSF/Motif Programmer’s Guide. Prentice Hall. Inc. Englewood Cliffs, NJ. Quercia, V., O’Reilly, T., 1990. X Window System User’s Guide. O’Reilly & Associates, Inc. Sebastopol, CA. Schamberger, M.A., Farmer, A.H., Terrell, J.W., 1982. Habitat suitability index models: introduction. US Department of Interior, US Fish and Wildlife Service, FWS/OBS-82-1.

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Shapiro, M., Westervelt, J., Gerdes, D., Larson, M., Brownfield, K., 1992. GRASS 4.0 Programmers Manual (draft). Sousa, P.J., 1985. Habitat Suitability Index Models: Gadwall. Biological Report 82(10.115), US Department of Interior, US Fish and Wildlife Service, Office of Biological Sciences, Washington DC, p. 36. Sousa, P.J., 1987. Habitat management models for selected wildlife management practices in the Northern Great Plains. USD.I. Bureau of Reclamation, REC-ERC-87-11,

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Denver. Sousa, P.J., 1989. A Users Manual for the Habitat Management Evaluation Method (HMEM). Software Version 2.0. USDI Fish and Wildlife Service, Washington, DC. US Army Corps of Engineers, 1991. GRASS 4.0 User’s Reference Manual. Construction Engineering Research Lab. US Fish and Wildlife Service, 1981. Standards for the Development of Habitat Suitability Index Models, 103 ESM. USDI Fish Wildlife Service, Ecological Service Division, Washington, DC.